Snow and glacier melting and accumulation are important processes of the hydrological cycle in the cryosphere, e.g., high-mountain areas. Glaciers and snow cover respond to climate change notably over the Tibetan Plateau (TP) as the Earth's Third Pole where complex topography and lack of ground-based observations result in knowledge gaps in hydrological processes and large uncertainties in model output. This study develops a snow and glacier melt model for a distributed hydrological model (Coupled Routing and Excess Storage model, CREST) using the Upper Brahmaputra River (UBR) basin in the TP as a case study. Satellite and ground-based precipitation and land surface temperature are jointly used as model forcing. A progressive two-stage calibration strategy is developed to derive model parameters, i.e., (1) snow melting processes (stage I) and (2) glacier melting and runoff generation and routing using multisource data (stage II). Stage-I calibration is performed using the MODIS snow cover area (SCA) product and a blending snow water equivalent (SWE) product combined with partial in situ measurements. Stage-II calibration is based on Gravity Recovery and Climate Experiment (GRACE) satellite-derived total water storage (TWS) changes and streamflow observed at a gauging station of the lower reach of the UBR. Results indicate that the developed two-stage calibration method provides more reliable streamflow, snow (both SCA and SWE), and TWS change simulations against corresponding observations than commonly used methods based on streamflow and/or SCA performance. The simulated TWS time series shows high consistency with GRACE counterparts for the study period 2003–2014, and overestimated melting rates and contributions of glacier meltwater to runoff in previous studies are improved to some degree by the developed model and calibration strategy. Snow and glacier runoff contributed 10.6% and 9.9% to the total runoff, and the depletion rate of glacier mass was ∼ −10 mm/a (∼ −2.4 Gt/a, Gt/a is gigaton (km3 of water) per year) over the UBR basin during the study period. This study is valuable in examining the impacts of climate change on hydrological processes of cryospheric regions and providing an improved approach for simulating more reliable hydrological variables over the UBR basin and potentially similar regions globally.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2016WR019656

Improved modeling of snow and glacier melting by a progressive two‐stage calibration strategy with GRACE and multisource data: How snow and glacier meltwater contributes to the runoff of the Upper Brahmaputra River basin?

Increase in Compound Drought and Heatwaves in a Warming World

Substantial climate change and intensive anthropogenic activities hamper efforts to explain and predict the variability of the terrestrial water balance. The Budyko framework has recently seen a renaissance in hydrological research due to the framework’s comprehensiveness and effectiveness for studying the effects of global change on water resources. In this paper, the development of the Budyko framework is analysed first. The temporal and spatial variability for the Budyko hypothesis are subsequently elaborated. On finer temporal scales, more processes need to be considered, and the degree of control exerted by individual factors on the water balance varies with the spatial scale considered. Finally, perspectives regarding better understanding and application of the Budyko framework at the catchment scale are provided. A representative and diverse catchment data set is required to estimate the parameter in the model. The co-evolution of landscape characteristics and climate properties would be beneficial...

Advances in hydrological modelling with the Budyko framework: A review

. The Budyko hypothesis (BH) is an effective approach to investigating long-term water balance at large basin scale under steady state. The assumption of steady state prevents applications of the BH to basins, which is unclosed, or with significant variations in root zone water storage, i.e., under unsteady state, such as in extremely arid regions. In this study, we choose the Heihe River basin (HRB) in China, an extremely arid inland basin, as the study area. We firstly use a calibrated and then validated monthly water balance model, i.e., the abcd model, to quantitatively determine annual and monthly variations of water balance for the sub-basins and the whole catchment of the HRB, and find that the roles of root zone water storage change and that of inflow from upper sub-basins in monthly water balance are significant. With the recognition of the inflow water from other regions and the root zone water storage change as additional possible water sources to evapotranspiration in unclosed basins, we further define the equivalent precipitation (Pe) to include local precipitation, inflow water and root zone water storage change as the water supply in the Budyko framework. With the newly defined water supply, the Budyko curve can successfully describe the relationship between the evapotranspiration ratio and the aridity index at both annual and monthly timescales, whilst it fails when only the local precipitation being considered. Adding to that, we develop a new Fu-type Budyko equation with two non-dimensional parameters (ω and λ) based on the deviation of Fu's equation. Over the annual timescale, the new Fu-type Budyko equation developed here has more or less identical performance to Fu's original equation for the sub-basins and the whole catchment. However, over the monthly timescale, due to large seasonality of root zone water storage and inflow water, the new Fu-type Budyko equation generally performs better than Fu's original equation. The new Fu-type Budyko equation (ω and λ) developed here enables one to apply the BH to interpret regional water balance over extremely dry environments under unsteady state (e.g., unclosed basins or sub-annual timescales).

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New interpretation of the role of water balance in an extended Budyko hypothesis in arid regions

Water availability is of importance for a wide range of ecological, climatological, and socioeconomic applications. Over land, the partitioning of precipitation into evapotranspiration and runoff essentially determines the availability of water. At mean annual catchment scales, the widely used Budyko framework provides a simple, deterministic, first-order relationship to estimate this partitioning as a function of the prevailing climatic conditions. Here we extend the framework by introducing a method to specify probabilistic estimates of water availability that account for the nonlinearity of the underlying phase space. The new framework allows to evaluate the predictability of water availability that is related to varying catchment characteristics and conditional on the underlying climatic conditions. Corresponding results support the practical experience of low predictability of river runoff in transitional climates.

Introducing a probabilistic Budyko framework

[1] Long-term climate is the first-order control on mean annual water balance, and vegetation and the interactions between climate seasonality and soil water storage change have also been found to play important roles. The purpose of this paper is to extend the Budyko hypothesis to the seasonal scale and to develop a model for interannual variability of seasonal evaporation and storage change. A seasonal aridity index is defined as the ratio of potential evaporation to effective precipitation, where effective precipitation is the difference between rainfall and storage change. Correspondingly, evaporation ratio is defined as the ratio of evaporation to effective precipitation. A modified Turc-Pike equation with a horizontal shift is proposed to model interannual variability of seasonal evaporation ratio as a function of seasonal aridity index, which includes rainfall seasonality and soil water change. The performance of the seasonal water balance model is evaluated for 277 watersheds in the United States. The 99% of wet seasons and 90% of dry seasons have Nash-Sutcliffe efficiency coefficients larger than 0.5. The developed seasonal model can be applied for constructing long-term evaporation and storage change data when rainfall, potential evaporation, and runoff observations are available. On the other hand, vegetation affects seasonal water balance by controlling both evaporation and soil moisture dynamics. The correlation between NDVI and evaporation is strong particularly in wet seasons. However, the correlation between NDVI and the seasonal model parameters is only strong in dry seasons.

X. Chen

Papers

Modeling Inter-annual Variability of Seasonal Evaporation and Storage Change Based on the Extended Budyko Framework